A new method for assessment of bunch compactness using automated image analysis

• S. Cubero ^[3] ; M.P. Diago ^[1] ; J. Blasco ^[3] ; J. Tardaguila ^[1] ; J.M. Prats-Montalbán ^[2] ; J. Ibáñez ^[1] ; J. Tello ^[1] ; N. Aleixos ^[2]
  1. [1] Instituto de Ciencias de la Vid y del Vino

     Instituto de Ciencias de la Vid y del Vino

     Logroño, España

     
  2. [2] Universidad Politécnica de Valencia

     Universidad Politécnica de Valencia

     Valencia, España

     
  3. [3] Instituto Valenciano de Investigaciones Agrarias

     Instituto Valenciano de Investigaciones Agrarias

     Moncada y Reixach, España

     

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• Localización: Australian journal of grape and wine research, ISSN 1322-7130, Vol. 21, Nº 1, 2015, págs. 101-109
• Idioma: inglés
• DOI: 10.1111/ajgw.12118
• Texto completo no disponible (Saber más ...)
• Resumen

  • Background and Aims Bunch compactness is a key feature determining grape and wine composition because tight bunches show a less homogeneous ripening, and are prone to greater fungal disease incidence. The Organisation Internationale de la Vigne et du Vin descriptor, the most recent method for the assessment of bunch compactness, requires visual inspection and trained evaluators, and provides subjective and qualitative values. The aim of this work was to develop a methodology based on image analysis to determine bunch compactness in a non-invasive, objective and quantitative way.

    Methods and Results Ninety bunches of nine different red cultivars of Vitis vinifera L. were photographed with a colour camera, and their bunch compactness was determined by visual inspection. A predictive partial least squares (PLS) model was developed in order to estimate bunch compactness from the morphological features extracted by automated image analysis, after the supervised segmentation of the images. The PLS model showed a capability of 85.3% for predicting correctly the rating of bunch compactness. The most discriminant variables of the model were highly correlated with the tightness of the berries in the bunch (proportion of visibility of berries, rachis and holes) and with the shape of the bunch (roundness, compactness shape factor and aspect ratio).

    Conclusions The non-invasive, image analysis methodology presented here enables the quantitative assessment of bunch compactness, thereby providing precise objective information for this key parameter.

    Significance of the Study A quantitative, objective and accurate system based on image analysis was developed as an alternative to current visual methods for the estimation of bunch compactness. This novel method could be applied to the classification of table grapes and/or at the receival point of wineries for sorting and assessment of wine grapes before vinification.